cloud-finops-skills
Built by OptimNow. Covers cloud financial management across
AWS, Azure, GCP, AI inference costs, GenAI capacity planning, SaaS asset management,
and tagging governance - grounded in enterprise delivery experience.
Install in 5 seconds
| Tool | One-step install |
|---|
 | At the Claude Code prompt: /plugin marketplace add https://github.com/OptimNow/cloud-finops-skills.git then /plugin install cloud-finops@optimnow |
 | Download the latest release zip, then Settings -> Skills -> Upload zip |
 | Self-host: ./install.sh --tool chatgpt --grouped (a public Cloud FinOps GPT is on the Roadmap) |
 | Self-host: ./install.sh --tool gemini (a public Cloud FinOps Gem is on the Roadmap) |
       | One-liner: curl -sL https://raw.githubusercontent.com/OptimNow/cloud-finops-skills/main/install.sh | bash -s -- --tool <name> |
 | curl -sL https://raw.githubusercontent.com/OptimNow/cloud-finops-skills/main/install.sh | bash |
 | pip install cloud-finops-mcp then add to your MCP client config (Claude Code / Cursor / Codex / Windsurf / Cline). Snippets: ./install.sh --tool mcp. Six tools - faceted retrieval over the 28 references and 23 named-pattern playbooks. |
Full options, troubleshooting, and the model-agnostic API loader: see INSTALLATION.md.
What is a Skill, and why does it matter
A Skill is a structured knowledge file that you attach to an AI agent or a large language
model. It gives the model accurate, domain-specific context that it would not otherwise
have access to.
Without it, general-purpose LLMs make confident but incorrect statements on FinOps topics.
They miscalculate PTU break-even rates. They confuse Azure and AWS reservation mechanics.
They give generic advice that ignores how billing actually works on Bedrock or Azure OpenAI.
The answers sound plausible. Most of the time, they are wrong on the details that matter.
This skill corrects that by injecting verified, curated FinOps knowledge directly into the
model's context - covering billing models, cost allocation patterns, optimisation
frameworks, and governance practices across the major cloud providers and AI platforms.
The closest analogy is RAG (Retrieval-Augmented Generation). Like RAG, it extends a
model's knowledge beyond its training data. Unlike RAG, it requires no vector database,
no embedding pipeline, and no retrieval infrastructure. You copy a folder into your agent
setup and the model gains structured expertise on cloud financial management.
This makes it portable: the same skill works with Claude, GPT, Gemini, or any
MCP-compatible agent - with no changes to the files.
